Delay-Optimal Probabilistic Scheduling for Low-Complexity Wireless Links With Fixed Modulation and Coding: A Cross-Layer Design

In this paper, we study the delay optimal scheduling policy for a multi-state wireless fading channel, by taking bursty packet arrivals and automatic repeat request-based packet transmission into account. In our system, the average delay each packet experiences includes the time it waits in the queue and the time it may take to retransmit due to packet delivery failure. To reduce the average delay, we propose a joint channel-aware and queue-aware stochastic scheduling policy to determine whether and with which probability the source should transmit based on channel and buffer states, subject to an average power constraint at the transmitter. To find the optimal scheduling probabilities, we formulate a non-linear power-constrained delay minimization problem with the aid of controlled Markov decision processes. The optimization problem is then converted into an equivalent linear programming problem by introducing new variables from the steady-state probabilities of the underlying Markov chain and transmission probabilities. By analyzing its property, we derive the structure of the optimal solution, and exploit it to obtain the optimal probabilities analytically. It is found that the optimal scheduling policy has a double threshold structure, and can significantly reduce the average delay.

Analysis of fading channels is usually made under the framework of either information theory or network theory. Information theory is usually concerned with the total throughput of the channel (Shannon capacity) while network theory considers also other performance measures such as average delay and buffer overflow probability. In a contemporary communication system operating in a fading environment, the performance measure is usually a combination of delay and throughput measures under various power constraints. Optimizing both delay and throughput is somewhat contradictory since achieving maximal throughput depends only on the fading channel while minimal average delay depends also on the arrival process of data to the transmitter. We define a performance criterion of both transmission power and average delay. Dynamic programming is employed to find the optimal power/rate control for this criterion as well as the system performance. Numerical results are presented as well as asymptotic analytic results for large buffers.

Age of Information (AoI) has attracted much attention recently due to its capability of characterizing the freshness of information in Industrial Internet of Things (IIoT). To improve the information freshness over a fading channel shared by multiple users, efficient scheduling methods are highly desired for IIoT. In this paper, we are interested in a cross-layer scheduling policy, in which power and rate-adaptive transmissions are enabled in the physical layer. More specifically, we adopt a probabilistic scheduling method to minimize AoI while satisfying an average power constraint. The optimization of the probabilistic scheduling policy is formulated as a Constrained Markov Decision Process (CMDP), the dimension of which grows exponentially with the number of users and the buffer size. To overcome the prohibitive computational cost, we reveal the threshold-based structure of the AoI-optimal scheduling. Then we conceive a low complexity iterative algorithm that gives us the optimal freshness-aware scheduling policy.

This paper examines the use of the fluid limit, a mathematical tool used to analyze Age of Information (AoI) in networks, to study the long-term average AoI for scheduling optimization in multiaccess networks with transmission frequency constraints. Two types of problems that have been studied in the literature but remain unsolved are revisited under this approach, wherein multiple agents transmit over an error-prone multiaccess channel, and long-term transmission frequency constraints are considered with either a resource constraint or a minimum throughput requirement. Previous works have derived the Whittle’s Index (WI) policy, max-weight policy and average AoI lower bound, however without optimality guarantee or closed-form performance analysis. This work advances the field by deriving closed-form optimal AoI and achieving scheduling policies for both problems, by utilizing the fluid limit tool and transforming the original high-dimensional Markov decision process into solving a set of partial derivative equations. As a result, threshold-based scheduling policies with closed-form threshold expressions are obtained which can be proven to be asymptotically optimal when the number of agents is large. Numerical simulations are conducted to demonstrate the performance optimality of the proposed schemes and analytical results.

In autonomous robots, the hardware and the algorithms are limited by the power constraints of the robots. Increasing algorithm efficiency can be considered as a way to deal with power constraints. Considering the processing load of Visual SLAM methods, which are frequently used in autonomous robots and become increasingly widespread, there is a need to investigate the effects of methods on power consumption. In this study, a performance comparison of ORB-SLAM2, which is a feature-based Visual SLAM method, DSO, and LDSO, which are direct Visual SLAM methods was conducted. We perform the comparison on ICL-NUIM, KITTI, and EUROC datasets using NVIDIA Jetson TX1 hardware. Central Processor Unit (CPU) usage, Graphics Processor Unit (GPU) power consumption, and total power consumption were taken into account since hardware and power constraints are very important issue for autonomous systems. Results showed that ORB-SLAM2 is the most efficient method for GPU load and power consumption. GPU power consumption and total power consumption are higher than expected for the DSO method compared to CPU load. The computational cost of Loop-Closure is seen clearly when comparing the DSO and LDSO methods.

This paper presents an energy-efficient power allocation for a multicarrier link over a frequency-selective fading channel with a delay-outage probability constraint. The power adaptation maximizes the system energy efficiency (EE), formulated as the ratio of the achieved effective capacity (EC) to the total expenditure power, including both transmission power and rate-independent circuit power. We prove that this objective function is quasi-concave in the transmission power, and derive the global optimum solution using fractional programming. Based on the obtained solution, we develop a power adaptation algorithm consisting of two steps: (i) establishing the optimum average power level corresponding to the maximum achievable EE with no transmit power constraint, and then (ii) for a given power constraint, jointly distributing the power over time and frequency based on the constraint and the optimum power level found in the first step. Analytical results show that the proposed EE-based power allocation has a structure similar to the allocation that maximizes the EC, but with a different cut-off threshold. Our proposed joint EE-optimal power allocation provides significant EE gains over both the joint spectral-efficient and independent-subcarrier EE-based power allocation schemes, where the rate-energy tradeoff becomes more pronounced with higher frequency selectivity.

We consider a user communicating over a fading channel with perfect channel state information. Data are assumed to arrive from some higher layer application and are stored in a buffer until transmitted. We study adapting the user's transmission rate and power based on the channel state information as well as the buffer occupancy; the objectives are to regulate both the long-term average transmission power and the average buffer delay incurred by the traffic. Two models for this situation are discussed; one corresponding to fixed-length/variable-rate codewords and one corresponding to variable-length codewords. The tradeoff between the average delay and the average transmission power required for reliable communication is analyzed. A dynamic programming formulation is given to find all Pareto optimal power/delay operating points. We then quantify the behavior of this tradeoff in the regime of asymptotically large delay. In this regime, we characterize simple buffer control policies which exhibit optimal characteristics. Connections to the delay-limited capacity and the expected capacity of fading channels are also discussed.

The main aim of this research is to develop Smart IOT based Power Adapter. In this proposed method, a Smart Power Adapter was developed along with the BLYNK mobile application to control and monitor the electrical power consumption. The performance of the developed smart power adapter is evaluated by testing the accuracy of power consumption for each home appliances, Battery capacity testing, cost of power consumed data for different home appliances, Cost saving with smart power adapter features and without features. Finally, the testing on changing the voltage sensor value for most prominent value for power consumption. It is observed that the approach for calculating the power consumed by home appliances shows least error of 0.1%. Where else, the battery power back up on the adapter only provide 5 hours of continues power usage. Besides that, the wireless control over the power adapter works flawless along the time scheduling. Finally, the smart power adapter estimates the costing of the power consumption relying on the usage of electrical power with least components.

We consider power adaptation strategies for binary phase-shift keying signals in Rayleigh fading channels under the assumption that channel state information is provided at both the transmitter and the receiver. We first derive a closed-form expression for the optimal power adaptation that minimizes average bit-error rate (BER) subject to average and peak transmission power constraints. Then, we analyze the average BER for channel inversion power adaptation with the same constraints. Our results show that the performance difference between the optimal power adaptation and the channel inversion becomes negligibly small as available average transmission power increases and/or peak-to-average power ratio decreases. We also find that an optimal peak-to-average power ratio exists that minimizes the average BER in the channel inversion scheme.

In this paper, the problem of joint mode switching and power adaptation is studied for simultaneous wireless information and power transfer (SWIPT) over a fading channel. The receiver dynamically switches between information decoding (ID) and energy harvesting (EH) modes while the transmitter dynamically adapts the transmit power. Considering the nonlinearity of practical EH circuits, a realistic nonlinear EH model is adopted rather than the idealistic linear EH model. To characterize the ultimate performance tradeoff between ID and EH, an optimization problem is formulated to maximize the average harvested energy under the constraints on the average achievable rate and the average transmit power, which is a nonconvex and combinatorial problem. To solve this problem, first, the optimal power adaptation scheme for the nonlinear EH receiver that operates only in the EH mode is proposed. Using this scheme, the jointly optimal solution for the mode switching and power adaptation is then derived. By comparing the obtained results to the existing results, various useful and interesting insights into the optimized SWIPT system with nonlinear EH are presented. An important insight into the impact of nonlinear EH is that, to exploit the high energy conversion efficiency of the nonlinear circuit, the EH mode has to be selected only in the moderate range of channel gains. Also, in the EH mode, the power has to be adapted to the short-term power threshold only for the moderate channel gains.

A MAC layer power management scheme for efficient energy delay tradeoff in a WLAN

The established success of wireless communications in terms of high data rates provision is undoubtedly owed to several combinations of different mechanisms. Such a combination is the well-known forward error correction (FEC) mechanism that combines automatic-repeat-request (ARQ) protocol and error correction capabilities. Furthermore, in the last decade, a cross-layer design provides the combination of different mechanisms across the layers and thus the performance gain is increased in wireless communications over fading channels. In this paper, we present a practical cross-layer design implementation that combines adaptive modulation and coding (AMC) and hybrid ARQ at the physical and data link layer by using different error correction schemes. Our implementation is based on a cross-layer design of AMC and HARQ. We perform simulations with different channel coding and modulation schemes that are implemented in broadband wireless access networks as IEEE 802.16 and 3GPP HSPA and thus the results obtained show the performance achieved by this practical cross-layer implementation in terms of spectral efficiency in two main broadband wireless access networks available into the market.

Over the past decade, the compute resource requirements of applications has continued to grow. This leads to an increase in the required infrastructure that poses additional challenges to mitigating energy consumption and peak power draws. Apache Mesos has been a dominant player in the cluster management space, acting as a middleware between frameworks and the underlying infrastructure. Due to its high availability, fault-tolerance, and scalability, Mesos is used in the industry to run massive scale workloads. Datacenters typically handle continuous dynamically varying workloads, potentially leading to high peak power and energy consumption, in-turn leading to high cost of operation. Given a set of scheduling policies that have been proven to be beneficial in lowering peak power and/or energy consumption, we propose a mechanism of switching between these policies in order to adapt to the dynamic variation in the workloads and the changes in the state of the cluster. Our experiments show that adapting to the variation in the workload can lead to a 9.2% reduction in max peak power consumption, 6.9% in total energy consumption, and a 6.4% reduction in makespan when compared to using a single scheduling policy.

In this work, a cross-layer framework is proposed for scheduling and resource allocation in the context of cooperative wideband wireless cellular systems supporting heterogeneous traffic. To this end, the network is partitioned into clusters, each comprising a small number of base stations that share data and/or control information and thus are able to apply cluster-wide cooperative multiple-antenna techniques such as network MIMO (N-MIMO) or large-scale MIMO (LS-MIMO). In contrast to previous works whose main goal was the study of purely physical layer metrics, the framework proposed here serves to evaluate the network performance in terms of operator-relevant metrics such as the average user throughput, the average delay, or different fairness indices. The cross-layer design introduced here is general enough to encompass various forms of power allocation or scheduling policies while being able to incorporate per-cluster or per-base power constraints. Remarkably, different greedy algorithms are introduced for both N-MIMO and LS-MIMO that effectively exploit the multiuser and frequency diversity of the system while remaining computationally feasible. A particularly interesting application of the proposed framework, explored in detail in this paper, is a thorough and realistic comparison of the performance of N-MIMO and LS-MIMO that reveals the strengths and weaknesses of each strategy in terms of different performance metrics.

In this paper, we consider a Cognitive Radio (CR) system where two type of users try to access to the primary spectrum : a primary user (PU) owning the spectrum license and a secondary user (SU) who does not own the spectrum license. However, the secondary communication is allowed to coexist with the primary communication as long as the interference caused by SU to PU is below a tolerable threshold. We study the optimization problem which maximizes the SU's achievable ergodic capacity under different types of power constraints and for different fading channel models. Our goal is to calculate the optimal power allocation strategies for these optimization problems. We show that modelling with Rayleigh fading for the channel between SU transmitter and PU receiver is an advantageous way to ameliorate the SU ergodic capacity. Furthermore, we consider four combinations of power constraints, since the interference power constraint and the transmit power constraint can be restricted by a peak or an average threshold. We also show that the SU ergodic capacity under average transmit power constraint and average interference power constraint outperforms the one with peak power constraints. In this case, we propose a novel decoupling method. Our method reduces the complexity of the initial problem and makes our initial problem easier to solve.